Steps to Improve the Teaching of Public Health to Undergraduate Medical Students in Canada
Bibliographic record
Abstract
In Canada, recent events and global influences have led to an emphasis on enhancing the public health system and improving the training of physicians in public and population health. Responding to the World Health Organization's initiative, Towards Unity for Health, the Association of Faculties of Medicine in Canada launched its Social Accountability initiative in 2001, which included the creation of the Public Health Task Group. With representation from the Public Health Agency of Canada, Canadian faculties of medicine, medical students, the Medical Council of Canada, and the community, the task group undertook four main steps: reaching agreement on common overall objectives for teaching public health, obtaining baseline information on the curricula of programs that were being provided across Canada, obtaining an inventory of resources available at each university, and creating a support system for fostering the development of public health teaching in undergraduate medicine programs. To date, the seventeen medical schools have nearly reached full consensus on the overall educational objectives. An initial scan of existing educational resources revealed no consistent use of any one text. Subsequent work has begun to create an inventory of sharable resources. A network of public health educators has been created and is seen as a promising start to addressing these other concerns. Other barriers remain to be addressed; these include lack of faculty (critical mass), inadequate support for local champions, inadequate methods of student assessment, and poor image as an attractive specialty/few role models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".